Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "104"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 104 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 104, Node N08:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459998 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.361207 51.733933 -0.318861 6.753470 2.555919 0.364266 2.383689 0.192298 0.6272 0.6161 0.3655 nan nan
2459997 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 5.922363 56.422646 -0.170970 7.293430 3.545161 -0.088497 2.307273 0.787642 0.6368 0.6314 0.3639 nan nan
2459996 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.402868 60.077864 0.894917 9.065462 2.458458 0.047686 1.372575 1.183063 0.6445 0.6350 0.3765 nan nan
2459995 RF_maintenance 100.00% 99.46% 99.41% 0.00% - - nan nan inf inf nan nan nan nan 0.4622 0.4916 0.4160 nan nan
2459994 RF_maintenance 100.00% 100.00% 100.00% 0.00% - - 238.419207 237.393747 inf inf 2250.161604 2334.088004 4860.106765 5330.502673 nan nan nan nan nan
2459993 RF_maintenance 100.00% 100.00% 100.00% 0.00% - - nan nan inf inf nan nan nan nan nan nan nan nan nan
2459991 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 7.393217 70.744085 -0.951861 7.151882 3.103983 -0.073493 0.134600 0.258084 0.6445 0.6273 0.3740 nan nan
2459990 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.567207 58.200781 -1.045533 6.982729 3.226063 -0.356952 0.714763 1.729350 0.6408 0.6241 0.3692 nan nan
2459989 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.150994 59.638209 -0.906636 6.474581 2.344901 -0.688741 -0.055128 -0.052370 0.6355 0.6226 0.3707 nan nan
2459988 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 7.139455 69.873941 -1.083889 7.105368 3.153034 -0.437819 0.318684 0.659346 0.6359 0.6265 0.3641 nan nan
2459987 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 5.827809 58.516281 -0.764033 7.027324 0.578827 -0.284752 0.758865 0.521978 0.6468 0.6307 0.3630 nan nan
2459986 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.936467 70.085667 -0.894388 7.487128 2.361753 -0.416600 -0.640576 2.104980 0.6662 0.6605 0.3177 nan nan
2459985 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 6.380658 65.899868 -0.709090 7.126842 0.542941 -0.551961 0.427609 1.582580 0.6452 0.6257 0.3724 nan nan
2459984 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 5.669874 61.458419 -0.448707 7.787141 1.775766 -0.061968 0.239374 4.263568 0.6590 0.6386 0.3476 nan nan
2459983 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 10.673918 60.981395 -1.098378 7.002298 1.402943 -0.463431 0.879439 1.850389 0.6715 0.6741 0.3020 nan nan
2459982 RF_maintenance 100.00% 0.22% 0.00% 0.00% - - 10.075884 39.577930 -1.051096 6.137870 0.889696 3.965903 -0.361376 4.911878 0.7204 0.7157 0.2604 nan nan
2459981 RF_maintenance 100.00% 15.18% 0.49% 0.00% - - 12.222619 49.642936 -1.156435 6.956979 20.269734 51.654386 189.240156 390.918017 0.5358 0.5923 0.3377 nan nan
2459980 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 12.750149 53.787093 -0.734491 6.662044 1.497027 0.618225 -0.560269 5.538463 0.6885 0.6837 0.2825 nan nan
2459979 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 11.384453 57.929381 -1.073415 6.451733 1.372709 -0.781788 1.240520 2.287713 0.6366 0.6234 0.3605 nan nan
2459978 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 10.345338 59.189508 -1.059693 6.892504 0.839308 -0.431266 0.997079 3.289932 0.6384 0.6222 0.3686 nan nan
2459977 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 8.691957 58.289027 -0.784913 6.657655 2.846134 -0.236224 0.805505 3.666400 0.6080 0.5935 0.3350 nan nan
2459976 RF_maintenance 100.00% 0.00% 0.00% 0.00% - - 9.139974 58.588689 -0.948318 7.073395 3.043151 -0.071239 0.425323 1.241431 0.6435 0.6272 0.3619 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 104: 2459998

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 51.733933 6.361207 51.733933 -0.318861 6.753470 2.555919 0.364266 2.383689 0.192298

Antenna 104: 2459997

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 56.422646 5.922363 56.422646 -0.170970 7.293430 3.545161 -0.088497 2.307273 0.787642

Antenna 104: 2459996

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 60.077864 6.402868 60.077864 0.894917 9.065462 2.458458 0.047686 1.372575 1.183063

Antenna 104: 2459995

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance ee Shape nan nan nan inf inf nan nan nan nan

Antenna 104: 2459994

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance ee Power inf 238.419207 237.393747 inf inf 2250.161604 2334.088004 4860.106765 5330.502673

Antenna 104: 2459993

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance ee Shape nan nan nan inf inf nan nan nan nan

Antenna 104: 2459991

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 70.744085 7.393217 70.744085 -0.951861 7.151882 3.103983 -0.073493 0.134600 0.258084

Antenna 104: 2459990

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 58.200781 58.200781 6.567207 6.982729 -1.045533 -0.356952 3.226063 1.729350 0.714763

Antenna 104: 2459989

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 59.638209 59.638209 6.150994 6.474581 -0.906636 -0.688741 2.344901 -0.052370 -0.055128

Antenna 104: 2459988

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 69.873941 69.873941 7.139455 7.105368 -1.083889 -0.437819 3.153034 0.659346 0.318684

Antenna 104: 2459987

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 58.516281 5.827809 58.516281 -0.764033 7.027324 0.578827 -0.284752 0.758865 0.521978

Antenna 104: 2459986

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 70.085667 70.085667 6.936467 7.487128 -0.894388 -0.416600 2.361753 2.104980 -0.640576

Antenna 104: 2459985

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 65.899868 65.899868 6.380658 7.126842 -0.709090 -0.551961 0.542941 1.582580 0.427609

Antenna 104: 2459984

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 61.458419 5.669874 61.458419 -0.448707 7.787141 1.775766 -0.061968 0.239374 4.263568

Antenna 104: 2459983

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 60.981395 10.673918 60.981395 -1.098378 7.002298 1.402943 -0.463431 0.879439 1.850389

Antenna 104: 2459982

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 39.577930 10.075884 39.577930 -1.051096 6.137870 0.889696 3.965903 -0.361376 4.911878

Antenna 104: 2459981

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Temporal Discontinuties 390.918017 49.642936 12.222619 6.956979 -1.156435 51.654386 20.269734 390.918017 189.240156

Antenna 104: 2459980

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 53.787093 53.787093 12.750149 6.662044 -0.734491 0.618225 1.497027 5.538463 -0.560269

Antenna 104: 2459979

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 57.929381 11.384453 57.929381 -1.073415 6.451733 1.372709 -0.781788 1.240520 2.287713

Antenna 104: 2459978

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 59.189508 59.189508 10.345338 6.892504 -1.059693 -0.431266 0.839308 3.289932 0.997079

Antenna 104: 2459977

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 58.289027 8.691957 58.289027 -0.784913 6.657655 2.846134 -0.236224 0.805505 3.666400

Antenna 104: 2459976

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
104 N08 RF_maintenance nn Shape 58.588689 58.588689 9.139974 7.073395 -0.948318 -0.071239 3.043151 1.241431 0.425323
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